A Machine Learning-Based Smart Grid Protection and Control Framework Using Kalman Filters for Enhanced Power Management
Bibliographic record
Abstract
Recent years have seen an increase in electrical crises due to the proliferation of automated inductors and electrical applications such as electric vehicles and mobile devices.The greater dispersion in the smart grid exposes it to risks like cyber-attacks, attenuation, and faulty detections that were not prevalent in conventional methods.The proposed machine learning-based renewable energy smart grid protector and controller (ReSGPC) using Kalman filters effectively controls and detects noise faults, cyberattacks, and attenuation, addressing the mentioned problems.Additionally, the proposed method has increased the efficiency of the smart grid due to its superior performance compared to conventional methods.This method provides an additional layer of protection for the system, safeguarding grid information.An optimal control law is developed to ensure the stability of the power network.The controller demonstrates significant improvements in effectiveness regardless of the initial values.Numerical simulations verify the developed approach, showing that the recommended method offers a more powerful line of attack.This strategy provides a crucial energy management framework for the smart grid, representing a reliable and system-based communication infrastructure with applications integrating renewable resources.Performance analysis reveals substantial improvements, with the proposed method achieving an efficiency increase of 0.25%, 0.42%, 0.32%, and 0.34% in Mean Squared Error (MSE) for ∆1, ∆2, ∆3, and ∆4 scenarios respectively, compared to existing methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".